Differential atlas for cancer assessment

ABSTRACT

Methods and apparatus associated with producing a quantification of differences associated with biochemical recurrence (BcR) in a region of tissue demonstrating prostate cancer (PCa) are described. One example apparatus includes a set of logics, and a data store that stores a set of magnetic resonance (MR) images acquired from a population of subjects. The set of logics includes an image acquisition logic that acquires a diagnostic image of a region of tissue in a patient demonstrating PCa, a morphology logic that extracts a shape feature, a volume feature, or an intensity feature from the diagnostic image or from a member of the set of MR images, a differential atlas construction logic that constructs a statistical shape differential atlas from the set of MR images, and a quantification logic that produces a quantification of differences based on the shape feature, the volume feature, or the intensity feature, and the differential atlas.

REFERENCE TO RELATED APPLICATIONS

This Application is a Continuation of U.S. application Ser. No.14/960,539 filed on Dec. 7, 2015, which claims priority to U.S.Provisional Application No. 62/099,665 filed on Jan. 5, 2015. Thecontents of the above-referenced matters are hereby incorporated byreference in their entirety.

BACKGROUND

Magnetic resonance (MR) imaging is routinely used to diagnose prostatecancer (PCa) and identify the stage of PCa. PCa may induce changes inthe shape of the prostate capsule and central gland (CG) in biopsypositive (Bx+) patients relative to biopsy negative (Bx−) patients,elevated-prostate specific antigen (PSA) patients, or normal patients.PCa may also induce changes in the volume of the prostate and CG in Bx+patients relative to Bx−, elevated-PSA, and normal patients. Thesechanges in the shape and volume of the prostate may be observed in T2weighted (T2w) MRI images.

Radiation therapy is a common treatment for PCa. However, radiationtherapy has been reported to have failure rates as high as 25%.Predicting biochemical recurrence (BcR) prior to radiation therapy mayenable better planning and personalization of treatment. MR images maybe used to assist the prediction of BcR in PCa patients. However, whenobvious extra-capsular spread of the disease is not present,conventional approaches employing MRI are not useful for distinguishingpatients who will experience BcR from those who will not.

Multi-parametric MRI is widely used in the management of PCa to improvethe localization and local staging of the disease. Despite its broadadoption in the management of PCa, conventional approaches using MRI maysuffer from a large variability in MRI acquisition parameters andreporting. This large variability may occur both within an individualinstitution (e.g., hospital, university) and across multipleinstitutions. Conventional approaches to MRI-based PCa diagnosis andidentification may employ protocols or guidelines for imagingacquisition parameters and findings reporting, although scoreinterpretation and detection thresholds, particularly across multipleinstitutions, have not been uniformly applied or exhaustively studied.Furthermore, implementing protocols and guidelines across differentinstitutions takes time, costs money, and puts a patient at additionalrisk if the guidelines and protocols are not consistently applied.

One conventional approach to reduce the subjectivity of image appearanceassessment and score assessment includes employing computer aideddetection and diagnosis (CADx) techniques. Conventional CADx approachestypically employ image-driven textures acquired from multiple MRIprotocols, and may combine the image-driven textures withpharmacokinetic behavior quantifiers and machine learning techniques.However, these conventional approaches have not been generalized acrossdifferent scanners or across different institutions.

Conventional MRI protocols may also lack tissue-specific numericalmeaning. A lack of tissue-specific numerical meaning may result ininconsistent MRI intensities, even for the same patient, the samescanned region, or the same scanner. The impact of inconsistent MRIintensities within the same patient, same scanned region, or samescanner may be exacerbated across multiple institutions. Thus,conventional approaches to PCa diagnosis and management using MRI areless than optimally accurate or efficient, and may not optimally utilizeinformation gathered across different populations by differentinstitutions.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example apparatus,methods, and other example embodiments of various aspects of theinvention. It will be appreciated that the illustrated elementboundaries (e.g., boxes, groups of boxes, or other shapes) in thefigures represent one example of the boundaries. One of ordinary skillin the art will appreciate that in some examples one element may bedesigned as multiple elements or that multiple elements may be designedas one element. In some examples, an element shown as an internalcomponent of another element may be implemented as an external componentand vice versa. Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates an example method of predicting BcR in a region oftissue demonstrating PCa pathology.

FIG. 2 illustrates an example method of predicting BcR in a region oftissue demonstrating PCa pathology.

FIG. 3 illustrates an example method of producing a quantification ofdifferences between a diagnostic image of a region of prostate tissueand a statistical shape atlas.

FIG. 4 illustrates an example apparatus that predicts BcR in a region oftissue demonstrating PCa pathology.

FIG. 5 illustrates an example computer in which example methods andapparatus described herein operate.

FIG. 6 illustrates a data flow associated with an example implementationof methods and apparatus described herein.

FIG. 7 illustrates statistically significant surface distancedifferences between subpopulations of subjects.

DETAILED DESCRIPTION

Variations in prostate shape and volume may be related to patientprognosis and outcome. Prostate structures may show differences involume and shape when comparing at risk Bx+ populations with Bx−populations. The prostate may demonstrate shape differences at theprostate apex when comparing Bx+ populations with normal populations.Example apparatus and methods rely on data acquired using a systematicinvestigation of the differences in the shape of the prostate capsule orCG on T2w MRI between patients with and without prostate cancer, orbetween patients exhibiting BcR and patients not exhibiting BcR. Thedata is combined into a statistical shape atlas. A statistical shapeatlas is a subgroup of population studies. Statistical shape atlasescombine information from multiple subjects into one unifiedrepresentation. Example methods and apparatus generate a quantificationof differences between different statistical shape atlases, or betweenan image of a patient demonstrating PCa pathology and a statisticalshape atlas. Example methods and apparatus may generate thequantification of differences by identifying and characterizingdifferences in the shape of the prostate capsule or CG. Exampleapparatus and methods use differences in the shape of the prostatecapsule or CG to distinguish a patient with normal prostate from apatient with a cancerous prostate. Example methods and apparatus thusfacilitate predicting BcR in patients demonstrating PCa.

In one implementation, shape differences between the capsule and CG onT2w MR images between 36 PCa− and 156 PCa+ subjects were investigatedusing a statistical shape atlas. A confirmation of the presence orabsence of cancer was based on a needle biopsy. The normal and PCa+populations were imaged using techniques including 1.5 Tesla (T) and 3TT2w MR imaging. Two separate atlases were constructed by mapping imagesacquired from the PCa− and PCa+ populations in a single canonicalrepresentation. Wilcoxon sum rank tests and multiple comparisonBonferroni corrections were subsequently applied to identify significantshape differences on the prostate capsule and the CG between the PCa−and PCa+ atlases. Statistically significant differences were identifiedfor the prostate capsule and CG shape between the PCa− and PCa+patients. A summary of these differences is illustrated in FIG. 7. Inone implementation, differences in distances as large as 3.1 mm and 1.1mm were identified on the anterior apex of the capsule and at the borderof the CG and peripheral zone.

Anterior prostate cancers are difficult to biopsy. This difficulty mayresult in delays in identification of the disease which may in turncause more substantial deformations in the capsule shape for theanterior tumors. Example methods and apparatus, by accessing andutilizing collections of MR images acquired across multipleinstitutions, improve on conventional approaches to detecting andpredicting BcR when the cancer affects the anterior prostate. Examplemethods and apparatus may also identify shape differences on T2w MRimages in the anterior apex of the prostate and at the border ofanatomic sub-regions between PCa+ and PCa− patients. Example methods andapparatus may employ automated computerized analysis of prostate shapeon T2w MR images to complement radiographic features of diseaseappearance (e.g., intensity, textures) in diagnostic decision making.Since a more accurate BcR prediction is made, example apparatus andmethods thus predict patient outcomes in a more consistent andreproducible manner than conventional approaches.

By generating statistical shape atlases from medical imagery acquiredfrom different institutions using different acquisition parameters,example methods and apparatus produce the concrete, real-world technicaleffect of utilizing disparate collections of imagery that wouldotherwise be unrelated and underutilized, while increasing the accuracyof the evaluation. Additionally, example apparatus and methods increasethe probability that at-risk patients receive timely treatment tailoredto the particular pathology they exhibit. Example methods and apparatusmay also reduce the number of invasive procedures needed to accuratelypredict BcR in PCa patients. The additional technical effect of reducingthe expenditure of resources and time on patients who are less likely tosuffer BcR or disease progression is also achieved. Example methods andapparatus thus improve on conventional methods in a measurable,clinically significant way.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic, and so on. The physicalmanipulations create a concrete, tangible, useful, real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, calculating, determining, and so on, refer to actions andprocesses of a computer system, logic, processor, or similar electronicdevice that manipulates and transforms data represented as physical(electronic) quantities.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

FIG. 1 illustrates an example computerized method 100 for predictingBcR. In one embodiment, method 100 includes identifying andcharacterizing differences induced by BcR in a region of tissuedemonstrating PCa pathology. Method 100 includes, at 110, accessing aset of MR images acquired from a population of patients. A member of theset of MR images includes a prostate capsule shape attribute, a prostatecapsule volume attribute, a CG shape attribute, or a CG volumeattribute. The CG includes a prostate central zone (CZ) and a prostatetransitional zone (TZ). The border of the TZ and the CZ is difficult toassess in 3D T2w MR imagery. Example methods and apparatus improve onconventional approaches by combining the TZ and CZ into the CG,increasing the speed and accuracy with which the CG may be studied. Thepopulation includes a set of subpopulations. In one embodiment, the setof subpopulations includes a PCa+ subpopulation, a PCa− subpopulation,or a normal subpopulation. A PCa− subpopulation may be a subpopulationin which members of the subpopulation demonstrate increased prostatespecific antigen (PSA). A PCa+ subpopulation may be confirmed by needlebiopsy (Bx+). A PCa− population may also be confirmed by needle biopsy(Bx−). In another embodiment, MR images of other, differentsubpopulations may be accessed.

Prostate shape and volume varies naturally. Prostate shape and volumemay also be influenced by benign conditions, including benign prostatichyperplasia. Example methods and apparatus assess intra-classvariability and compare intra-class variations with inter-classdifferences. For example, example methods and apparatus may assessshape, volume, or intensity variability within a PCa+ subpopulation, andalso compare differences between the PCa+ subpopulation and a PCa−subpopulation.

In one embodiment of method 100, subpopulations may be acquired across aplurality of institutions. For example, a first subpopulation may beacquired by a first institution, while a second subpopulation may beacquired by a second, different institution. In one example, the firstinstitution may be a university that acquires a set of MR images frompatients identified as PCa+, while the second institution may be ahospital that acquires a set of MR images from patients identified asPCa−. In other embodiments, MR images may be acquired from other typesor numbers of institutions.

Members of the set of MR images may be acquired using differentacquisition parameters. In one embodiment of method 100, a first memberof the set of MR images is acquired using a first set of MR acquisitionparameters having a first set of values, and a second member of the setof MR images is acquired using a second, different set of MR acquisitionparameters having a second, different set of values. The first set of MRacquisition parameters and the second set of MR acquisition parametersinclude image dimensions measured in pixels, resolution, or slicespacing. In one embodiment, a member of the set of MR images may beacquired with dimensions of 192 by 1024 pixels, 240 by 1024 pixels, 192by 320 pixels, 240 by 320 pixels, 512 by 512 pixels, or 1024 by 1024pixels. In one embodiment, a member of the set of MR images may beacquired with a resolution or 0.21-0.97 mm, 0.21-0.9 mm, 0.37-0.97 mm,0.50-0.90 mm, 0.35 mm, or 0.21 mm. In one embodiment, a member of theset of MR images may be acquired with a slice spacing of 1.5-3.5 mm,3.0-3.4 mm, 1.5-3.0 mm, 3.0-4.0 mm, or 3.0-3.5 mm. In other embodiments,other acquisition parameters and values may be employed.

Accessing the set of MR images may include accessing an MR image of aregion of prostate tissue. The MR image may be stored, for example, in acomputer memory or may be provided across a computer network. In oneembodiment, the MR image is a 1.5 Tesla T2w MR image or a 3T T2w MRimage. In another embodiment, other images sizes or other imagingtechniques may be employed.

Method 100 also includes, at 120, constructing a statistical shape atlasfrom the set of MR images. The statistical shape atlas brings T2w MRimages into a common frame of reference while ensuring the deformablealignment of the prostate capsule and CG within a population. Thestatistical shape atlas thus accounts for variations in intensity andshape within anatomic regions across different populations. In oneembodiment, a first member of the set of MR images associated with asubpopulation is elastically aligned relative to a second member of theset of MR images associated with the subpopulation. The first member andthe second member may be elastically aligned or registered usinganatomically constrained registration that facilitates keeping theprostate capsule and CG aligned during atlas construction. Otherregistration approaches may be employed.

Method 100 also includes, at 130, accessing an MR image of a region ofprostate tissue in a patient demonstrating cancerous pathology. The MRimage of the region of prostate tissue has a prostate capsule shapeattribute, a prostate capsule volume attribute, a CG shape attribute, ora CG volume attribute. The MR image may be a 1.5 Tesla T2w MR image or a3T T2w MR image. In one embodiment, the member of the set of MR imagesor the MR image is acquired using a surface coil or using an endorectalcoil. In another embodiment, other images sizes or other imagingtechniques may be employed.

Method 100 also includes, at 140, producing a quantification ofdifferences between the MR image of the region of prostate and thestatistical shape atlas. The quantification of differences may representthe difference in shape, volume, intensity, or texture between a regionof prostate in a patient and a statistical shape atlas. Thequantification of differences may be based, at least in part, on acomparison of the prostate represented in the MR image of the region ofprostate tissue in the patient demonstrating cancerous pathology withthe statistical shape atlas. The comparison may be based, at least inpart, on attributes associated with the prostate represented in the MRimage. The attributes of the prostate represented in the MR imagecompared with the statistical shape atlas may include the prostatecapsule shape attribute, the prostate capsule volume attribute, the CGshape attribute, or the CG volume attribute of the MR image of theregion of prostate tissue demonstrating cancerous pathology. Thecomparison may also be based on an MR intensity captured by the MRimage. In one embodiment, method 100 generates a characterization of theMR image of the region of prostate tissue by identifying andcharacterizing differences induced by BcR in the MR image of the regionof prostate tissue in the patient demonstrating cancerous pathology. Thecharacterization may be based on the comparison.

Method 100 also includes, at 145, generating a BcR probability scoreassociated with the MR image of the region of prostate tissue in thepatient demonstrating cancerous pathology. The BcR probability score mayrepresent the probability that the patient associated with the MR imagewill develop BcR within a period of time. The BcR probability score maybe based on the quantification of differences.

Method 100 also includes, at 150, classifying the MR image of the regionof prostate tissue in the patient demonstrating cancerous pathology.Classifying the MR image may include controlling a CADx system togenerate a classification of the region of tissue in the image based, atleast in part, on the quantification of differences or on the BcRprobability score. The classification may indicate a likelihood that thepatient will experience BcR within a time period. The time period may beone year, five years, or another time period.

In one embodiment, method 100 further includes annotating the prostatecapsule or the CG represented in a member of the set of MR images or inthe MR image of the region of prostate tissue demonstrating cancerouspathology. Annotating the prostate capsule or the CG may includeautomatically annotating the prostate capsule or the CG. In oneembodiment, T2w MR images of the prostate capsule or the CG may beannotated using MeVisLab software. In another embodiment, an expertpathologist may annotate the prostate capsule or the CG.

Example methods and apparatus facilitate applying a more appropriatelydetermined treatment based on the quantification of differences or theBcR probability. Using a more appropriately determined and appliedtreatment may lead to less therapeutics being required for a patient ormay lead to avoiding or delaying a biopsy, a resection, or otherinvasive procedure. When regions of cancerous tissue, including theprostate capsule and CG detected in MR images, are more quickly and moreaccurately classified as likely or unlikely to experience BcR, patientswith poorer prognoses may receive a higher proportion of scarceresources (e.g., therapeutics, physician time and attention, hospitalbeds) while those with better prognoses may be spared unnecessarytreatment, which in turn spares unnecessary expenditures and resourceconsumption. Example methods and apparatus may thus have the real-world,quantifiable effects of improving patient outcomes and reducing resourceexpenditure.

While FIG. 1 illustrates various actions occurring in serial, it is tobe appreciated that various actions illustrated in FIG. 1 could occursubstantially in parallel. By way of illustration, a first process couldaccess a set of MR images from a population of patients, a secondprocess could construct a statistical shape atlas, and a third processcould generate quantification of differences. While three processes aredescribed, it is to be appreciated that a greater or lesser number ofprocesses could be employed and that lightweight processes, regularprocesses, threads, and other approaches could be employed.

FIG. 2 illustrates an example method 200 for predicting BcR in a regionof tissue demonstrating PCa pathology. Method 200 is similar to method100 but includes additional actions 220-268 associated with constructinga statistical shape atlas. Method 200 includes, at 210, accessing a setof MR images acquired from a population of patients, similar to action110 described above.

Method 200 also includes, at 220, annotating a prostate capsulerepresented in a member of the set of MR images. Method 200 alsoincludes, at 230, annotating a CG represented in the member of the setof MR images. The prostate capsule or the CG may be annotated by anexpert pathologist, or the prostate capsule or the CG may be annotatedautomatically. In one embodiment, annotating the prostate capsule or theCG is performed using MeVisLab software.

Method 200 also includes, at 240, determining if the member of the setof MR images was acquired using an endorectal coil. Images acquired withendorectal coils may exhibit artifacts manifested as hyper-intensesignal around the endorectal coil. Conventional approaches may notaccount for these artifacts. If method 200 determines that the member ofthe set of MR images was acquired using an endorectal coil, method 200,at 242, applies bias field correction for the member of the set of MRimages that was acquired using an endorectal coil.

Method 200 includes, at 244, applying template-based anatomicallyconstrained registration to a subset of the set of MR images associatedwith a subpopulation. In one embodiment, anatomically constrainedregistration is based, at least in part, on a prostate capsule shapeattribute, a prostate capsule volume attribute, a CG shape attribute, ora CG volume attribute. The anatomically constrained registration mayalso be based on a T2w intensity associated with the member of the setof MR images. Anatomically constrained registration ensures thatsubjects within a subpopulation are mapped into a common frame ofreference, by explicitly considering the T2w MRI intensity as well asthe annotated outlines of the prostate capsule or CG when optimizing thespatial alignment. Anatomically constrained registration ensures thatboth prostate and CG are in alignment relative to the registrationreferences, and also to the other subjects in the subpopulation. Theregistration template captures the subpopulation anatomiccharacteristics, such as the shape and volume of the prostate and CG.

Method 200 also includes, at 250, computing a spatial median of shapeattributes or volume attributes. The spatial median may be computed forthe prostate capsule shape attribute or the CG shape attribute for thesubset of the set of MR images associated with a subpopulation. Thespatial median may also be calculated for the prostate capsule volumeattribute or the CG volume attribute for the subset. In anotherembodiment, other statistical measures of the shape attribute or volumeattribute may be computed.

Method 200 also includes, at 252, progressively updating the templateused at step 244 for the template-based anatomical registration.Updating the template may be based, at least in part, on the spatialmedian of the prostate capsule shape attribute or the spatial median ofthe CG shape attribute. Updating the template may also be based, atleast in part, on the spatial median of the prostate capsule volumeattribute, or the spatial median of the CG volume attribute. In anotherembodiment, the template may be progressively updated based on otherproperties of a member of the set of MR images.

Method 200 also includes, at 260, progressively increasing thecomplexity of an optimized three dimensional (3D) transformation. The 3Dtransformation may be used to register a member of the set of MR imagesto another, different member of the set of MR images. The 3Dtransformation may be a translation. In one embodiment, progressivelyincreasing the complexity of the optimized 3D transformation includesconverting the 3D transformation from a first type of transformation toa second, different transformation. For example, the optimized 3Dtransformation may be converted from a translation to an affinetranslation. In one embodiment, upon computing the affine translation,method 200 may compute an elastic deformation. In another embodiment,the optimized 3D transformation may be converted to other types oftransformations, using other, different approaches.

In one embodiment, method 200 determines whether a threshold complexityof the optimized 3D transformation has been achieved. Upon determiningthat the threshold complexity of the optimized 3D transformation has notbeen achieved, method 200 may repeat actions 244, 250, and 252. Upondetermining that the threshold complexity of the optimized 3Dtransformation has been achieved, method 200 proceeds to action 264.

Method 200 also includes, at 264, correcting an atlas shift between anatlas associated with a first subpopulation and an atlas associated witha second subpopulation. In one embodiment, correcting the atlas shiftbetween the atlas associated with the first subpopulation and the atlasassociated with the second subpopulation includes optimizing the affinetranslation relative to the first subpopulation and the secondsubpopulation. In one example, the affine translation optimization isbased, at least in part, on the prostate capsule shape attribute or theCG shape attribute. In another embodiment, the affine translation may beoptimized using other techniques.

Method 200 also includes, at 268, comparing members of a first subset ofthe set of MR images with members of a second subset of MR Images. Inone embodiment, comparing members of different subsets includes applyinga per-voxel comparison between members of a subset of MR imagesassociated with the first subpopulation and members of a subset of MRimages associated with the second, different subpopulation. In oneembodiment, the per-voxel comparison includes a non-parametric Wilcoxontest. In another embodiment, the per-voxel comparison further includesapplying multiple comparison correction using a Bonferonni correction.In another embodiment, comparing members of different subsets mayinclude other, different tests or corrections.

Method 200 also includes, at 270, accessing an MR image of a region ofprostate tissue demonstrating PCa. The MR image includes a prostatecapsule shape attribute, a prostate capsule volume attribute, a CG shapeattribute, or a CG volume attribute. Method 200 also includes, at 280generating a quantification of differences based, at least in part, acomparison of the MR image of the region of prostate tissuedemonstrating PCa with the statistical shape atlas. Method 200 alsoincludes, at 285, generating a BcR probability score based, at least inpart, on the quantification of differences. Method 200 also includes, at290, classifying the region of tissue based, at least in part, on thequantification of differences or the BcR probability score.

FIG. 6 illustrates a data flow associated with an example implementationof differential atlas construction and statistical comparison that maybe employed by methods and apparatus described herein. FIG. 6 summarizesone possible implementation for the construction of a differential shapeatlas used for comparing images acquired from subpopulations of subjectsdemonstrating PCa+, PCa−, or normal pathologies. Conventional approachesfor quantifying differences between different populations of prostates,or for predicting BcR, may perform per-voxel comparisons, butconventional approaches do not effectively handle the large variety ofprostate or CG volumes across different subpopulations. Example methodsand apparatus may map a subpopulation or subpopulations into a commonframe of reference using anatomically constrained registration. Forexample, a set of one through N, where N is an integer, subpopulation 1MR images 610 may be registered using anatomically constrainedregistration. Another set of one through M, where M is an integer,subpopulation 2 MR images 620 may also be registered using anatomicallyconstrained registration. Since, in this example, the construction ofthe subpopulation 1 atlas 630 is performed independently of theconstruction of the subpopulation 2 atlas 640, translation or rotationshifts relative to the different atlases may occur.

Example methods and apparatus correct atlas shift. In the exampleillustrated in FIG. 6, atlas shift is corrected by optimizing an atlasaffine transformation of the first atlas 630 relative to the secondatlas 640. In this example, the alignment of the atlas affineregistration 650 is driven by the prostate shape. In another example,the alignment of the atlas affine registration 650 may be driven by theCG shape, or by other features. Example methods and apparatus applystatistical tests to compare the shape or volume features of theprostate capsules or CGs described in the differential shape atlases. Inthe example illustrated in FIG. 6, a per-voxel non-parametric Wilcoxontest is employed to evaluate the statistical significance ofmorphological shape differences. Multiple comparison correction may beperformed by example methods and apparatus using a Bonferonnicorrection, or other correction techniques. In the example illustratedin FIG. 6, the multiple comparison correction includes dividing surfacevoxels' p-values by the number of comparisons performed. In thisexample, the number of comparisons performed is equal to the number ofsurface voxels of either the prostate capsule or the CG. In oneembodiment, statistical comparison may be addressed after multiplecomparison corrections results in a p-value of p<0.05. Prostatedifferences 660 illustrates regions of statistically significant surfacedifferences associated with the prostate capsule shape between thedifferent subpopulations. CG differences 670 illustrates statisticallysignificant surface differences associated with the CG shape between thedifferent subpopulations. Legend 680 indicates the outline and visualcues used to indicate regions of statistically significant surfacedistance differences represented by prostate differences 660 and CGdifferences 670.

FIG. 7 illustrates figures with statistically significant surfacedistance differences between subpopulations. FIG. 7 thus illustrates onepossible example of a graphical representation of a quantification ofdifferences. FIG. 7 illustrates surface representations of prostatecapsules or CGs that may be associated with PCa pathology. FIG. 7,elements 701, 711, 721, and 731 (column 1), and 703, 713, 723, and 733(column 2) illustrate surface representations that show shapedifferences between atlases on the prostate capsule. FIG. 7, elements705, 715, 725, and 735 (column 3) and 707, 717, 727, and 737 (column 4)illustrate surface representations that show shape differences betweenatlases on the CG. Columns 1 and 3 show the anterior side of theprostate capsule or the CG, while columns 2 and 4 show the posteriorside of the prostate capsule or CG, near the rectal wall. Shades of greyindicate spatial distances between the two atlases on a scale of 0 mm to5 mm, while the darker outline indicated in legend 750 is used to showthe regions of statistical significance of the difference, where thedifference is computed based on the mapped subjects.

PCa+ and PCa− subjects may demonstrate morphological differences on theleft and right side of the prostate, while statistically significantdifferences may be apparent on CGs on the posterior side. The CG shapedifferences are the outcome of CG overall volume differences, based onCG hypertrophy in the Bx− subjects. Example methods and apparatuseffectively capture these morphological differences and apply them whengenerating a quantification of differences or calculating a BcRprobability score.

FIG. 7 further illustrates a comparison of PCa+ and PCa− subjects fromdifferent institutions in elements 711-737. The comparison of C₁ ⁺ (PCa+subjects from a first institution) with C₁ ⁻ (PCa− subjects from thefirst institution) subpopulations indicates statistically significantdifferences on both anterior and posterior sides of the prostate nearthe apex in elements 711-713. In this example, CG morphologicaldifferences are highlighted near the inferior side, and are also presentat the superior side near the base in elements 715-717. The PCa+ andPCa− comparison within C₂ (second institution) and C₃ (thirdinstitution) subpopulations acquired from a second institution and athird institution shows a consistent trend. Statistically significantdifferences are indicated near the apex on the prostate in elements721-723 and 731-733. CG differences may be based on the volumedifferences between the PCa+ and PCa− subjects, due to the hypertrophyof CG.

Subpopulations may be compared by volume. In one embodiment, prostatecapsule volume or CG volume may be computed after members of the set ofMR images are mapped in a common frame of reference provided by theatlas. Atlas construction corrects volume differences in the prostatefound in the subpopulations acquired from different institutions. Atlasalignment may indicate CG hypertrophy of a Bx− population relative to aPCa+ subpopulation, with a p-value<0.05. CG hypertrophy of the Bx−population relative to a PCa+ subpopulation may be demonstrated whencomparing PCa+ and PCa− populations acquired across differentinstitutions. A comparison of Bx+ with normal subjects may revealconsistent volumes in both prostate and CG, indicating that the normalpopulation is similar in prostate and CG volume relative to the PCa+subjects. In this implementation, the mean prostate and CG volumes arerelatively smaller than original volumes, since aligning the atlas tendsto shrink the subjects when aligning them into a common frame ofreference. Example methods and apparatus improve on conventionalapproaches by considering volume information when quantifyingdifferences between populations, between a patient and a statisticalshape atlas, or when generating a BcR probability score. PSA-basedpatient screening causes an over-representation of large prostatevolumes, which produce more PSA than smaller prostates. Thus, patientsdemonstrating elevated PSA levels but who are found to be Bx−, maysuffer hypertrophy of the CG. Example methods and apparatus thus reducePSA-related over-diagnosis compared to conventional approaches.

FIG. 3 illustrates a method 300 for producing a quantification ofdifferences between a diagnostic image of a region of prostate tissueand a statistical shape atlas. Method 300 includes, at 310 accessing astatistical shape atlas. The statistical shape atlas includes a set ofregistered medical images of prostate tissue acquired from a populationof subjects. In one embodiment, the population includes a subpopulationof PCa+ subjects, a subpopulation of PCa− subjects, and a subpopulationof normal subjects. A prostate represented in an image in the set ofmedical images includes a shape and a volume. The shape and the volumemay be associated with the prostate capsule, or with the CG. The CG mayinclude the CZ and the TZ.

Method 300 also includes, at 320, accessing a diagnostic image of aregion of tissue demonstrating cancerous pathology. The diagnostic imageof the region of tissue demonstrating cancerous pathology may be an MRimage of a region of tissue in a patient demonstrating PCa pathology.

Method 300 also includes, at 330, computing a shape of the prostaterepresented in the diagnostic image. Method 300 also includes, at 340,computing a volume of the prostate represented in the diagnostic image.The shape and the volume of the prostate may be computed based, at leastin part, on an annotation of an outline of the prostate capsule or theCG represented in the diagnostic image. The prostate capsule or the CGmay be annotated automatically, or may be annotated by an expertpathologist.

Method 300 also includes, at 345, producing a quantification ofdifferences between the diagnostic image of a region of prostate tissueand the statistical shape atlas. The quantification of differences maybe based, at least in part, on the shape of the prostate represented inthe diagnostic image, the volume of the prostate represented in thediagnostic image, and the statistical shape atlas. The quantification ofdifferences may include a numerical or graphical representation of adifference between the diagnostic image of the region of prostate tissueand the statistical shape atlas.

Method 300 also includes, at 347, generating a classification for theregion of tissue demonstrating cancerous pathology. The classificationmay classify the region of tissue demonstrating cancerous pathology asPCa+, PCa−, or normal. The classification may be based, at least inpart, on the quantification of differences.

Method 300 also includes, at 350, providing a prognosis prediction basedon the classification or the quantification of differences. Providingthe prognosis prediction may include controlling a CADx system togenerate a BcR probability score. The BcR probability score may be basedon the classification or the quantification of differences. The BcRprobability score may include a probability that a patient associatedwith the diagnostic image will experience BcR within a period of time.The period of time may be six months, one year, five years, or anotherperiod of time. Providing the prognosis prediction may also includedisplaying a numerical or graphical representation of the quantificationof differences.

In one example, a method may be implemented as computer executableinstructions. Thus, in one example, a computer-readable storage mediummay store computer executable instructions that if executed by a machine(e.g., computer) cause the machine to perform methods described orclaimed herein including method 100, method 200, and method 300. Whileexecutable instructions associated with the listed methods are describedas being stored on a computer-readable storage medium, it is to beappreciated that executable instructions associated with other examplemethods described or claimed herein may also be stored on acomputer-readable storage medium. In different embodiments, the examplemethods described herein may be triggered in different ways. In oneembodiment, a method may be triggered manually by a user. In anotherexample, a method may be triggered automatically.

FIG. 4 illustrates an example apparatus 400 for predicting BcR in aregion of tissue demonstrating PCa pathology in an image. Apparatus 400includes a processor 410, a memory 420, a data store 425, a set oflogics 440, and an interface 430 that connects the processor 410, thememory 420, the data store 425, and the set of logics 440. Data store425 stores a set of MR images acquired from a population of subjectsusing a surface coil approach or an endorectal coil approach. The set oflogics 440 includes an image acquisition logic 442, a morphology logic444, a differential atlas construction logic 446, and a prediction logic448. In one embodiment, the functionality associated with the set oflogics 440 may be performed, at least in part, by hardware logiccomponents including, but not limited to, field-programmable gate arrays(FPGAs), application specific integrated circuits (ASICs), applicationspecific standard products (ASSPs), system on a chip systems (SOCs), orcomplex programmable logic devices (CPLDs). In one embodiment,individual members of the set of logics 440 are implemented as ASICs orSOCs.

Image acquisition logic 442 acquires a diagnostic image of a region oftissue in a patient demonstrating PCa pathology. In one embodiment,image acquisition logic 442 acquires a T2w MR image of a patientdemonstrating PCa pathology using a surface coil approach or anendorectal coil approach. Other imaging approaches may be used togenerate and access the image accessed by image acquisition logic 442.Other image dimensions or image acquisition parameters may also be used.

Morphology logic 444 extracts a shape feature, a volume feature, or anintensity feature from the diagnostic image or from a member of the setof MR images. Morphology logic 444 generates an outline of the prostateor an outline of the CG of a member of the set of MR images or thediagnostic image by automatically detecting and annotating the prostateor the CG represented in a member of the set of MR images or thediagnostic image. The CG includes a TZ and a CZ.

Differential atlas construction logic 446 constructs a statistical shapedifferential atlas from the set of MR images stored in the data store425. In one embodiment, differential atlas construction logic 446constructs the statistical shape differential atlas by stratifying theset of MR images into a subset of MR images associated with asubpopulation of subjects. Differential atlas construction logic 446 mayalso stratify the set of MR images into a subset associated with aninstitution that acquired a member of the subset of MR images. A subjectbelongs to a PCa+ subpopulation, a PCa− subpopulation, or a normalpopulation. A member of the set of MR images includes a shape feature ora volume feature. The shape feature may be associated with a prostatecapsule or a CG represented in the member of the set of MR images. Thevolume feature may be associated with the prostate capsule or the CGrepresented in the member of the set of MR images. A first member of theset of MR images may be acquired using a first set of acquisitionparameters, and a second member of the set of MR images may be acquiredusing a second, different set of acquisition parameters.

Differential atlas construction logic 446 also registers a member of theset of MR images using template-based anatomically constrainedregistration. The template-based anatomically constrained registrationmay be based on the outline of the prostate capsule represented in themember of the set of MR images, the outline of the CG in the member ofthe set of MR images, or a T2w MR intensity of the member of the set ofMR images. In another embodiment, other registration techniques may beemployed by differential atlas construction logic 446.

Differential atlas construction logic 446 also computes a spatial medianfor the set of MR images based, at least in part, on the shape featureor the volume feature. Differential atlas construction logic 446 maycompute the spatial median based on the prostate shape feature, the CGshape feature, the prostate volume feature, or the CG volume feature. Inone embodiment, differential atlas construction logic 446 also computesa median for the set of MR images based, at least in part, on the T2w MRintensity of a member of the set of MR images. The spatial median or theT2w MR intensity median may be based on a subset of the set of MR imagesassociated with a subpopulation. Differential atlas construction logic446 also iteratively updates the template based, at least in part, onthe spatial median.

Differential atlas construction logic 446 also characterizes astatistical difference between a shape feature associated with a firstsubpopulation and shape feature associated with a second subpopulation.Differential atlas construction logic 446 may also characterize astatistical difference between a volume feature associated with thefirst subpopulation and a volume feature associated with the secondsubpopulation. Differential atlas construction logic 446 may employ anon-parametric per-voxel Wilcoxon test to characterize the statisticalsignificance of morphological shape differences.

Quantification logic 448 computes a quantification of differencesassociated with the shape feature, the volume feature, or the intensityfeature, and the differential atlas. In one embodiment, quantificationlogic 448 also generates a BcR probability score for the patient based,at least in part, the quantification of differences. The quantificationof differences may be based, at least in part, on the shape feature, thevolume feature, or the intensity feature, and the differential atlas. Inone embodiment, quantification logic 448 generates a registereddiagnostic image by registering the diagnostic image to the statisticalshape differential atlas. Quantification logic 448 may then control aCADx system or other medical imaging system to display the registereddiagnostic image, the quantification of differences, or the BcRprobability score. In one embodiment, quantification logic 448 maycontrol a CADx system to classify the diagnostic image based, at leastin part, on the quantification of differences or on the BcR probabilityscore. In other embodiments, other types of CADx systems may becontrolled, including CADx systems for types of cancer where diseaseclassification and prognosis prediction may be based on shape or volumefeatures quantified from MR images of a region of tissue.

In one embodiment of apparatus 400, the set of logics 440 also includesa display logic. The display logic may control the CADx system todisplay the quantification of differences, the BcR prediction score, thediagnostic image, members of the statistical shape differential atlas,the volume features, the registered diagnostic image, or the shapefeatures, on a computer monitor, a smartphone display, a tablet display,or other displays. Displaying the quantification of differences, the BcRprobability score, the diagnostic image, members of the atlas, or thefeatures may also include printing the quantification of differences,the BcR probability score, the diagnostic image, members of the atlas,or the features. The display logic may also control the CADx to displayan image of the region of tissue demonstrating PCa. The image of theregion of tissue demonstrating PCa may include annotated representationsof the prostate capsule or the CG. By displaying the diagnostic imagealong with the quantification of differences, the BcR probability score,or elements of the atlas, example apparatus provide a timely andintuitive way for a human pathologist to more accurately classifypathologies demonstrated by a patient, thus improving on conventionalapproaches to predicting cancer recurrence and disease progression.

FIG. 5 illustrates an example computer 500 in which example methodsillustrated herein can operate and in which example logics may beimplemented. In different examples, computer 500 may be part of an MRIsystem, may be operably connectable to an MRI system, or may be part ofa CADx system.

Computer 500 includes a processor 502, a memory 504, and input/outputports 510 operably connected by a bus 508. In one example, computer 500may include a set of logics 530 that perform a method of predicting BcRin a region of tissue demonstrating PCa pathology. Thus, the set oflogics 530, whether implemented in computer 500 as hardware, firmware,and/or a combination thereof may provide means (e.g., circuits,hardware) for predicting BcR in a region of tissue demonstrating PCapathology. In different examples, the set of logics 530 may bepermanently and/or removably attached to computer 500. In oneembodiment, the functionality associated with the set of logics 530 maybe performed, at least in part, by hardware logic components including,but not limited to, field-programmable gate arrays (FPGAs), applicationspecific integrated circuits (ASICs), application specific standardproducts (ASSPs), system on a chip systems (SOCs), or complexprogrammable logic devices (CPLDs). In one embodiment, individualmembers of the set of logics 530 are implemented as ASICs or SOCs.

Processor 502 can be a variety of various processors including dualmicroprocessor and other multi-processor architectures. Memory 504 caninclude volatile memory and/or non-volatile memory. A disk 506 may beoperably connected to computer 500 via, for example, an input/outputinterface (e.g., card, device) 518 and an input/output port 510. Disk506 may include, but is not limited to, devices like a magnetic diskdrive, a tape drive, a Zip drive, a flash memory card, or a memorystick. Furthermore, disk 506 may include optical drives like a CD-ROM ora digital video ROM drive (DVD ROM). Memory 504 can store processes 514or data 517, for example. Disk 506 or memory 504 can store an operatingsystem that controls and allocates resources of computer 500.

Bus 508 can be a single internal bus interconnect architecture or otherbus or mesh architectures. While a single bus is illustrated, it is tobe appreciated that computer 500 may communicate with various devices,logics, and peripherals using other busses that are not illustrated(e.g., PCIE, SATA, Infiniband, 1394, USB, Ethernet).

Computer 500 may interact with input/output devices via I/O interfaces518 and input/output ports 510. Input/output devices can include, butare not limited to, digital whole slide scanners, an optical microscope,a keyboard, a microphone, a pointing and selection device, cameras,video cards, displays, disk 506, network devices 520, or other devices.Input/output ports 510 can include but are not limited to, serial ports,parallel ports, or USB ports.

Computer 500 may operate in a network environment and thus may beconnected to network devices 520 via I/O interfaces 518 or I/O ports510. Through the network devices 520, computer 500 may interact with anetwork. Through the network, computer 500 may be logically connected toremote computers. The networks with which computer 500 may interactinclude, but are not limited to, a local area network (LAN), a wide areanetwork (WAN), or other networks.

References to “one embodiment”, “an embodiment”, “one example”, and “anexample” indicate that the embodiment(s) or example(s) so described mayinclude a particular feature, structure, characteristic, property,element, or limitation, but that not every embodiment or examplenecessarily includes that particular feature, structure, characteristic,property, element or limitation. Furthermore, repeated use of the phrase“in one embodiment” does not necessarily refer to the same embodiment,though it may.

“Computer-readable storage medium”, as used herein, refers to a mediumthat stores instructions or data. “Computer-readable storage medium”does not refer to propagated signals. A computer-readable storage mediummay take forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, tapes, and other media. Volatile media mayinclude, for example, semiconductor memories, dynamic memory, and othermedia. Common forms of a computer-readable storage medium may include,but are not limited to, a floppy disk, a flexible disk, a hard disk, amagnetic tape, other magnetic medium, an application specific integratedcircuit (ASIC), a compact disk (CD), other optical medium, a randomaccess memory (RAM), a read only memory (ROM), a memory chip or card, amemory stick, and other media from which a computer, a processor orother electronic device can read.

“Logic”, as used herein, includes but is not limited to hardware, orfirmware, or combinations of each to perform a function(s) or anaction(s), or to cause a function or action from another logic, method,or system. Logic may include a software controlled microprocessor, adiscrete logic (e.g., ASIC), an analog circuit, a digital circuit, aprogrammed logic device, a memory device containing instructions, andother physical devices. Logic may include one or more gates,combinations of gates, or other circuit components. Where multiplelogical logics are described, it may be possible to incorporate themultiple logical logics into one physical logic. Similarly, where asingle logical logic is described, it may be possible to distribute thatsingle logical logic between multiple physical logics.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

Throughout this specification and the claims that follow, unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood to beterms of inclusion and not exclusion. For example, when such terms areused to refer to a stated integer or group of integers, such terms donot imply the exclusion of any other integer or group of integers.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

While example systems, methods, and other embodiments have beenillustrated by describing examples, and while the examples have beendescribed in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the systems, methods, and other embodiments described herein.Therefore, the invention is not limited to the specific details, therepresentative apparatus, and illustrative examples shown and described.Thus, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims.

What is claimed is:
 1. A non-transitory computer-readable storage devicestoring computer executable instructions that when executed by acomputer control the computer to perform a method for predictingbiochemical recurrence (BcR) in a region of tissue demonstratingprostate cancer (PCa) pathology, the method comprising: accessing a setof magnetic resonance (MR) images acquired from a population ofsubjects; acquiring a diagnostic image of a region of tissue in apatient demonstrating PCa pathology; extracting a shape feature, avolume feature, or an intensity feature from the diagnostic image or theset of MR images; constructing a statistical shape differential atlasfrom the set of MR images; computing a quantification of differencesassociated with the shape feature, the volume feature, or the intensityfeature, and the differential atlas; generating a BcR probability scorefor the patient based, at least in part, on the quantification ofdifferences; controlling a computer aided diagnosis (CADx) system togenerate a classification of the region of tissue in the image based, atleast in part, on the BcR probability score or the quantification ofdifferences; and controlling the CADx system to display theclassification, the quantification of differences, or the BcRprobability score.
 2. The non-transitory computer-readable storagedevice of claim 1, where the set of MR images is acquired from apopulation of patients, where the population includes a set ofsubpopulations, where a member of the set of MR images includes aprostate capsule shape attribute, a prostate capsule volume attribute, acentral gland (CG) shape attribute, or a CG volume attribute.
 3. Thenon-transitory computer-readable storage device of claim 1, where amember of the set of MR images or the diagnostic image is a 1.5 Tesla(T) T2 weighted (T2w) MR image or a 3T T2w MR image.
 4. Thenon-transitory computer-readable storage device of claim 3, whereacquiring the diagnostic image includes acquiring a T2w MR image of apatient demonstrating PCa pathology using a surface coil approach or anendorectal coil approach.
 5. The non-transitory computer-readablestorage device of claim 1, where a first member of the set of MR imagesis acquired using a first set of MR acquisition parameters having afirst set of values, and a second member of the set of MR images isacquired using a second, different set of MR acquisition parametershaving a second, different set of values, where the first set of MRacquisition parameters and the second set of MR acquisition parametersincludes pixel dimensions, resolution, or slice spacing.
 6. Thenon-transitory computer-readable storage device of claim 1, whereextracting the shape feature, the volume feature, or the intensityfeature includes generating an outline of the prostate or an outline ofthe central gland (CG) of a member of the set of MR images or thediagnostic image by automatically detecting and annotating the prostateor the CG represented in a member of the set of MR images or thediagnostic image, where the CG includes a prostate transitional zone anda prostate central zone.
 7. The non-transitory computer-readable storagedevice of claim 2, where constructing the statistical shape differentialatlas comprises: stratifying the set of MR images into a subset of MRimages associated with a subpopulation of subjects, where a subjectbelongs to a PCa positive (PCa+) subpopulation, a PCa negative (PCa−)subpopulation, or a normal population, where a member of the set of MRimages includes a shape feature or a volume feature, where the shapefeature is associated with a prostate capsule or a CG represented in themember of the set of MR images, where the volume feature is associatedwith the prostate capsule or the CG represented in the member of the setof MR images; registering a member of the set of MR images usingtemplate-based anatomically constrained registration based on theoutline of the prostate capsule represented in the member of the set ofMR images, the outline of the CG in the member of the set of MR images,or a T2w MR intensity of the member of the set of MR images; computing aspatial median for the set of MR images based, at least in part, on theshape feature or the volume feature; iteratively updating a templatebased, at least in part, on the spatial median; and characterizing astatistical difference between a shape feature associated with a firstsubpopulation and shape feature associated with a second subpopulation,or between a volume feature associated with the first subpopulation anda volume feature associated with the second subpopulation.
 8. Thenon-transitory computer-readable storage device of claim 1, wherecomputing the quantification of differences comprises: generating aregistered diagnostic image by registering the diagnostic image to thestatistical shape differential atlas.
 9. The non-transitorycomputer-readable storage device of claim 8, the method furthercomprising controlling the CADx system to display the registereddiagnostic image.
 10. An apparatus employed in a computer assisteddiagnosis (CADx) device, the apparatus comprising: one or moreprocessors configured to: access a set of magnetic resonance (MR) imagesacquired from a population of patients, where the population includes aset of subpopulations, where a member of the set of MR images includes aprostate capsule shape attribute, a prostate capsule volume attribute, acentral gland (CG) shape attribute, or a CG volume attribute, and wherethe set of subpopulations includes a PCa positive (PCa+) subpopulation,a PCa negative (PCa−) subpopulation, or a normal subpopulation;construct a statistical shape atlas from the set of MR images; access anMR image of a region of prostate tissue in a patient demonstratingcancerous pathology, where the MR image of the region of prostate tissuehas a prostate capsule shape attribute, a prostate capsule volumeattribute, a CG shape attribute, or a CG volume attribute; produce aquantification of differences between the MR image of the region ofprostate and the statistical shape atlas; compute a BcR probabilityscore based, at least in part, on the quantification of differences;generate a classification of the region of tissue in the image based, atleast in part, on the BcR probability score or the quantification ofdifferences; and display the classification, the BcR probability score,or the quantification of differences.
 11. The apparatus of claim 10,where producing the quantification of differences comprises comparingthe prostate capsule shape attribute of the MR image of the region ofprostate tissue demonstrating cancerous pathology, the prostate capsulevolume attribute of the MR image of the region of prostate tissuedemonstrating cancerous pathology, the CG shape attribute of the MRimage of the region of prostate tissue demonstrating cancerouspathology, or the CG volume attribute of the MR image of the region ofprostate tissue demonstrating cancerous pathology, with the statisticalshape atlas.
 12. The apparatus of claim 10, where the subpopulations areacquired across a plurality of institutions.
 13. The apparatus of claim10, the one or more processors further configured to automaticallyannotate the prostate capsule or the CG in a member of the set of MRimages or in the MR image of the region of prostate tissue demonstratingcancerous pathology.
 14. The apparatus of claim 10, where the CGincludes a prostate central zone and a prostate transitional zone. 15.The apparatus of claim 10, where a member of the set of MR images or theMR image of the region of prostate tissue demonstrating cancerouspathology is a 1.5 Tesla (T) T2 weighted (T2w) MR image or a 3T T2w MRimage.
 16. The apparatus of claim 15, where a first member of the set ofMR images is acquired using a first set of MR acquisition parametershaving a first set of values, and a second member of the set of MRimages is acquired using a second, different set of MR acquisitionparameters having a second, different set of values, where the first setof MR acquisition parameters and the second set of MR acquisitionparameters includes pixel dimensions, resolution, or slice spacing. 17.The apparatus of claim 10, where constructing the statistical shapeatlas comprises: annotating a prostate capsule represented in a memberof the set of MR images; annotating a CG represented in the member ofthe set of MR images; upon determining that the member of the set of MRimages was acquired using an endorectal coil, bias field correcting themember of the set of MR images that was acquired using an endorectalcoil; applying template-based anatomically constrained registration to asubset of the set of MR images associated with a subpopulation, wherethe registration is based, at least in part, on a prostate capsule shapeattribute, a prostate capsule volume attribute, a CG shape attribute, aCG volume attribute, or on a T2w intensity associated with the member ofthe set of MR images; computing a spatial median of the prostate capsuleshape attribute or the CG shape attribute for the subset of the set ofMR images associated with a subpopulation, or a spatial median of theprostate capsule volume attribute or the CG volume attribute for thesubset; progressively updating a template based, at least in part, onthe spatial median of the prostate capsule shape attribute, the spatialmedian of the CG shape attribute, the spatial median of the prostatecapsule volume attribute, or the spatial median of the CG volumeattribute; progressively increasing the complexity of an optimized threedimensional (3D) transformation, where the 3D transformation is atranslation; correcting an atlas shift between an atlas associated witha first subpopulation and an atlas associated with a second, differentsubpopulation; and applying a per-voxel comparison between members of asubset of the set of MR images associated with the first subpopulationand members of a subset of a set of MR images associated with thesecond, different subpopulation.
 18. The apparatus of claim 17, whereprogressively increasing the complexity of the optimized threedimensional (3D) transformation includes: converting the optimized 3Dtransformation from a translation to an affine translation, orconverting the optimized 3D transformation from an affine translation toan elastic deformation.
 19. The apparatus of claim 18, where correctingan atlas shift between the atlas associated with the first subpopulationand the atlas associated with the second subpopulation includesoptimizing the affine translation relative to the first subpopulationand the second subpopulation, where the affine translation optimizationis based, at least in part, on the prostate capsule shape attribute orthe CG shape attribute.
 20. A non-transitory computer-readable storagedevice storing executable instructions that, in response to execution,cause a biochemical recurrence (BcR) prediction device to performoperations for producing a quantification of differences between adiagnostic image of a region of prostate tissue and a statistical shapeatlas, the operations comprising: accessing a statistical shape atlas,where the statistical shape atlas includes a set of registered medicalimages of prostate tissue acquired from a population of subjects, thepopulation including a subpopulation of prostate cancer positive (PCa+)subjects, a subpopulation of prostate cancer negative (PCa−) subjects,and a subpopulation of normal subjects, where a prostate represented inan image in the set of medical images includes a shape and a volume;accessing a diagnostic image of a region of tissue demonstratingcancerous pathology; computing a shape of the prostate represented inthe diagnostic image; computing a volume of the prostate represented inthe diagnostic image; producing a quantification of differences betweenthe region of tissue demonstrating cancerous pathology and thestatistical shape atlas by comparing the shape of the prostaterepresented in the diagnostic image, the volume of the prostaterepresented in the diagnostic image, and the statistical shape atlas;generating a classification for the region of tissue demonstratingcancerous pathology based, at least in part, on the quantification ofdifferences; providing a prognosis prediction based on theclassification or the quantification of differences; generating apersonalized PCa treatment plan based on the quantification ofdifferences, the classification, and the prognosis prediction; anddisplaying the personalized PCa treatment plan, the diagnostic image,the quantification of differences, the classification, or the prognosisprediction.